DP-600 · Question #40
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might…
The correct answer is B. No. The df.explain() PySpark expression does not calculate descriptive statistics like min, max, mean, and standard deviation.
Question
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You have a Fabric tenant that contains a new semantic model in OneLake. You use a Fabric notebook to read the data into a Spark DataFrame. You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns. Solution: You use the following PySpark expression: df.explain() Does this meet the goal?
Options
- AYes
- BNo
How the community answered
(35 responses)- A14% (5)
- B86% (30)
Why each option
The `df.explain()` PySpark expression does not calculate descriptive statistics like min, max, mean, and standard deviation.
`df.explain()` provides query plan details, not statistical summaries of data content.
The `df.explain()` method is used to print the logical and physical execution plan of a DataFrame query, helping to understand how Spark will process the data, not to compute statistical aggregates.
Concept tested: PySpark DataFrame query plan inspection
Source: https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.explain.html
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